Despite being still far from adequately composing these problems [1]}, nowadays the variability of massive data describing human movements allows planners and policy-makers to face relevant urban challenges through computational methods [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}. While the observation of mobility flows offer for instance the possibility to investigate the inner workings of urban transport systems, this is not enough for efficient transport planning. As Ganin et al. [11]} note, traditional transport studies focus on efficiency measure by observing a transport network under normal operating conditions. This approach though fails to capture information that really matters for real-world applications, i.e. how the network reacts when is under stress conditions. Such is the case of traumatic events that might cause disruption of service or worse, like in the event of natural or man-made disasters (e.g., floods, terrorist attacks, etc.). Thus, uncovering weak points that affect a network's resilience, a proven key metric for transport policies [11]}, is not only a task of paramount relevance for the design of proper intervention scenarios in case of disasters, but also an actual requirement for managing transport services in complex global cities like Singapore. We refer to such weak points as Urban Achilles Heels.https://en.wikipedia.org/wiki/Achilles%27_heel
